Parameter Estimation for Permanent Magnet Synchronous Motors Based on the Improved Genetic Algorithm

The accurate electrical parameters of permanent magnet synchronous motors (PMSMs) are significant for the control system design in various industrial applications. PMSMs parameter estimation is difficult due to the model’s nonlinearity. Thus, this paper proposed a novel improved genetic algorithm (IGA) to estimate PMSM’s electrical parameters including resistance, inductance, and flux linkage. A local random search algorithm (LRSA) is employed for IGA to increase the population diversity and obtain the global optimal point. In addition, the inverter nonlinear caused by the dead time is compensated for the accurate sample data. Finally, the feasibility of the proposed parameter estimation method is verified by experiments. The results indicate that the proposed IGA has high estimation accuracy and high searching ability.